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Dr. Long Chen
University of Macau

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0 Computational Intelligence
0 Data Mining
0 Machine Learning
0 control
0 system modeling

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Journal article
Published: 16 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor $l_{2,2,1}$ norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor $l_{2,2,1}$ norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC).

ACS Style

Yingxu Wang; Tianjun Li; Long Chen; Yufeng Yu; Yinping Zhao; Jin Zhou. Tensor-Based Robust Principal Component Analysis With Locality Preserving Graph and Frontal Slice Sparsity for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -19.

AMA Style

Yingxu Wang, Tianjun Li, Long Chen, Yufeng Yu, Yinping Zhao, Jin Zhou. Tensor-Based Robust Principal Component Analysis With Locality Preserving Graph and Frontal Slice Sparsity for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-19.

Chicago/Turabian Style

Yingxu Wang; Tianjun Li; Long Chen; Yufeng Yu; Yinping Zhao; Jin Zhou. 2021. "Tensor-Based Robust Principal Component Analysis With Locality Preserving Graph and Frontal Slice Sparsity for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-19.

Original paper
Published: 12 July 2021 in Nonlinear Dynamics
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An indirect adaptive consensus control method is presented for multi-agent systems (MASs) with unknown hysteresis states and input. All system states that can be utilized to design the controller are measured by the sensors subjected to hysteresis, and thus, the system state values are inaccurate. Meanwhile, it is difficult to compensate the input hysteresis for it is coupled with the state hysteresis. The unknown function from agent’s neighbors also increases the difficulty of controller design. To eliminate the influence of unknown input hysteresis, an inverse adaptive compensated method is presented. The problem of state hysteresis is addressed by designing two adaptive laws to approximate the upper and lower bounds of unknown hysteresis coefficient. Neural networks are introduced to handle the unknown dynamics of agent and its neighbors. The proposed control scheme can guarantee that the consensus errors of followers converge to a predefined interval of zero asymptotically. In addition, the transient performance of MASs can be further ensured. The simulation examples are included to verify the effectiveness of the presented control approach.

ACS Style

Zhuangbi Lin; Zhi Liu; Yun Zhang; C. L. Philip Chen. Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis. Nonlinear Dynamics 2021, 1 -17.

AMA Style

Zhuangbi Lin, Zhi Liu, Yun Zhang, C. L. Philip Chen. Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis. Nonlinear Dynamics. 2021; ():1-17.

Chicago/Turabian Style

Zhuangbi Lin; Zhi Liu; Yun Zhang; C. L. Philip Chen. 2021. "Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis." Nonlinear Dynamics , no. : 1-17.

Journal article
Published: 06 July 2021 in IEEE Access
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For multimodal medical image fusion problems, most of the existing fusion approaches are based on pixel-level. However, the pixel-based fusion method tends to lose local and spatial information as the relationships between pixels are not considered appropriately, which has much influence on the quality of the fusion results. To address this issue, a region-based multimodal medical image fusion framework is proposed based on superpixel segmentation and a post-processing optimization method in this paper. In this framework, the average image of the source medical images is firstly obtained by a weighted averaging method. To effectively obtain homogeneous regions and preserve the complete information of image details, the fast linear spectral clustering(LSC) superpixel algorithm is carried out to segment the average image and get superpixel labels. For each region of the medical images, log-gabor filter(LGF) and sum modified laplacian(SML) are adopted to extract texture feature and contrast feature for the measurement of region importance. The most important regions are selected and the decision map is generated by comparison. Moreover, to get a more accurate decision map, a new post-processing optimized method based on genetic algorithm(GA) is given. A weighted strategy is applied to the extracted features and the weighting factor can be adaptively adjusted by GA. The effectiveness of the proposed fusion method is validated by conducting experiments on eight pairs of medical images from diverse modalities. In addition, seven other mainstream medical image fusion methods are adopted for comparing the performance of fusion. Experimental results in terms of qualitative and quantitative evaluation demonstrate that the proposed method can achieve state-of-the-art performance for multimodal medical image fusion problems.

ACS Style

Junwei Duan; Shuqi Mao; Junwei Jin; Zhiguo Zhou; Long Chen; C. L. Philip Chen. A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation. IEEE Access 2021, 9, 96353 -96366.

AMA Style

Junwei Duan, Shuqi Mao, Junwei Jin, Zhiguo Zhou, Long Chen, C. L. Philip Chen. A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation. IEEE Access. 2021; 9 ():96353-96366.

Chicago/Turabian Style

Junwei Duan; Shuqi Mao; Junwei Jin; Zhiguo Zhou; Long Chen; C. L. Philip Chen. 2021. "A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation." IEEE Access 9, no. : 96353-96366.

Journal article
Published: 28 May 2021 in IEEE Transactions on Neural Networks and Learning Systems
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Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of parameters. As an alternative, a fast and efficient discriminative broad learning system (BLS) is proposed, which takes the advantages of flat structure and incremental learning. The BLS has achieved outstanding performance in classification and regression problems. However, the previous studies ignored the reason why the BLS can generalize well. In this article, we focus on the interpretation from the viewpoint of the frequency domain. We discover the existence of the frequency principle in BLS, i.e., the BLS preferentially captures low-frequency components quickly and then fits the high frequencies during the incremental process of adding feature nodes and enhancement nodes. The frequency principle may be of great inspiration for expanding the application of BLS.

ACS Style

Guang-Yong Chen; Min Gan; C. L. Philip Chen; Hong-Tao Zhu; Long Chen. Frequency Principle in Broad Learning System. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -7.

AMA Style

Guang-Yong Chen, Min Gan, C. L. Philip Chen, Hong-Tao Zhu, Long Chen. Frequency Principle in Broad Learning System. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-7.

Chicago/Turabian Style

Guang-Yong Chen; Min Gan; C. L. Philip Chen; Hong-Tao Zhu; Long Chen. 2021. "Frequency Principle in Broad Learning System." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-7.

Journal article
Published: 12 May 2021 in IEEE Geoscience and Remote Sensing Letters
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Deep learning is a powerful technique for image processing. Convolution neural network (CNN) is one of the widely used approaches for hyperspectral image (HSI) classification. These methods mostly need a time-consuming pretraining process to obtain deep features. Random patches networks (RPNets) provide a novel approach that the convolution kernel can be the original image without any pretraining process. In this letter, we propose a novel HSI classification method, multiscale random convolution broad learning system (MRC-BLS), which takes the spatial feature learning by an adaptive weighted mean filter as the convolution kernel to extract local spatial feature in the first layer. Different sizes of random convolution kernels can obtain a multiscale feature map. The weighted fusion of multiscale spatial features extracted by different sizes kernels can get better performance in HSI classification. A broad learning system (BLS) is an efficient classifier to classify images by the multiscale random feature. Experiments in three HSI data sets fully testify to the efficiency and satisfactory performance of the proposed method.

ACS Style

You Ma; Zhi Liu; C. L. Philip Chen. Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

You Ma, Zhi Liu, C. L. Philip Chen. Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

You Ma; Zhi Liu; C. L. Philip Chen. 2021. "Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 10 May 2021 in IEEE Transactions on Neural Networks and Learning Systems
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The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization. By the proposed controller, the hysteresis's effect can be compensated effectively and the tracking error can be bounded by an explicit function of design parameters. Simultaneously, all other signals in the closed-loop system can be remaining bounded. Finally, two examples are presented to verify the effectiveness of the proposed method.

ACS Style

Jianhui Wang; Hongkang Zhang; Kemao Ma; Zhi Liu; C. L. Philip Chen. Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -9.

AMA Style

Jianhui Wang, Hongkang Zhang, Kemao Ma, Zhi Liu, C. L. Philip Chen. Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-9.

Chicago/Turabian Style

Jianhui Wang; Hongkang Zhang; Kemao Ma; Zhi Liu; C. L. Philip Chen. 2021. "Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-9.

Journal article
Published: 04 May 2021 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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This article is mainly concerned with the fixed-time tracking control problem for multiple uncertain mechanical systems with actuation dead zones. As the dead zones and control gains are time varying and completely unknown, the control impact on mechanical system becomes completely unknown, making the achievement of fixed-time tracking control nontrivial. The existing studies on fixed-time tracking control cannot effectively estimate and compensate such dead zones, resulting in the upper bound of convergence time being uncertain and even system becoming unstable. To compensate the dead zones in a fixed time, we introduce a bound estimation method to estimate the impact of dead zones and design two fixed-time adaptive laws. Two cases on system uncertainties are considered. By introducing distributed control method, adaptive control method, neural networks and some skillful treatments, two novel distributed adaptive neural fixed-time tracking control schemes are proposed. It is confirmed that, with the proposed control schemes, all followers which are uncertain mechanical systems with completely unknown dead zones track the leader with sufficient precision in a predefined time.

ACS Style

Dacai Liu; Zhi Liu; C. L. Philip Chen; Yun Zhang. Distributed Adaptive Neural Fixed-Time Tracking Control of Multiple Uncertain Mechanical Systems With Actuation Dead Zones. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -14.

AMA Style

Dacai Liu, Zhi Liu, C. L. Philip Chen, Yun Zhang. Distributed Adaptive Neural Fixed-Time Tracking Control of Multiple Uncertain Mechanical Systems With Actuation Dead Zones. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-14.

Chicago/Turabian Style

Dacai Liu; Zhi Liu; C. L. Philip Chen; Yun Zhang. 2021. "Distributed Adaptive Neural Fixed-Time Tracking Control of Multiple Uncertain Mechanical Systems With Actuation Dead Zones." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-14.

Journal article
Published: 01 May 2021 in IEEE Transactions on Industrial Electronics
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The varying-coefficient state-dependent autoregressive with exogenous inputs (SD-ARX) models are very useful in nonlinear system modeling and control. Considering that state feedback control strategy with a series of variable feedback gains provides more freedom than a constant feedback gain for the robust predictive controller design, this paper proposes a robust model predictive control (RPC) algorithm with variable feedback gains for output tracking. In the proposed method, by defining input and output increment sequences of the system, two polytopic state space models, in which the dynamic behavior of the system is wrapped, are constructed. Then, based on the polytopic state space models, an RPC with variable feedback gains for output tracking is designed. To further reduce the conservatism, the parameter-dependent Lyapunov functions are constructed for the design of the variable feedback gains in the control strategy. The proposed RPC can expand the feasible region of the robust controller and improve the control performance. The simulation on a continuous stirred tank reactor verified the feasibility and efficacy of the proposed method.

ACS Style

Feng Zhou; Min Gan; C. L. Philip Chen. Robust Model Predictive Control Algorithm With Variable Feedback Gains for Output Tracking. IEEE Transactions on Industrial Electronics 2021, 68, 4228 -4237.

AMA Style

Feng Zhou, Min Gan, C. L. Philip Chen. Robust Model Predictive Control Algorithm With Variable Feedback Gains for Output Tracking. IEEE Transactions on Industrial Electronics. 2021; 68 (5):4228-4237.

Chicago/Turabian Style

Feng Zhou; Min Gan; C. L. Philip Chen. 2021. "Robust Model Predictive Control Algorithm With Variable Feedback Gains for Output Tracking." IEEE Transactions on Industrial Electronics 68, no. 5: 4228-4237.

Journal article
Published: 28 April 2021 in IEEE Transactions on Cybernetics
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This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial computing networks. The survey provides a complex network and spatiotemporal dynamics (abbr. network dynamics) perspective for understanding the brain and cortical networks and, furthermore, develops integrated approaches of neuroscience and network dynamics toward building brain-inspired intelligence with learning and resilience functions. Presented are fundamental concepts and principles of complex networks, neuroscience, and hybrid dynamic systems, as well as relevant studies about the brain and intelligence. Other promising research directions, such as brain science, data science, quantum information science, and machine behavior are also briefly discussed toward future applications.

ACS Style

Bin Hu; Zhi-Hong Guan; Guanrong Chen; C. L. Philip Chen. Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence. IEEE Transactions on Cybernetics 2021, PP, 1 -14.

AMA Style

Bin Hu, Zhi-Hong Guan, Guanrong Chen, C. L. Philip Chen. Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence. IEEE Transactions on Cybernetics. 2021; PP (99):1-14.

Chicago/Turabian Style

Bin Hu; Zhi-Hong Guan; Guanrong Chen; C. L. Philip Chen. 2021. "Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence." IEEE Transactions on Cybernetics PP, no. 99: 1-14.

Journal article
Published: 22 April 2021 in IEEE Transactions on Cybernetics
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The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a group of interval type-2 fuzzy neurons was used to replace the feature neurons of BLS. Then, the representation of BLS can be improved to obtain good robustness. Second, a fuzzy pseudoinverse learning algorithm was designed to adjust the parameter of type-2 FBLS. Then, the proposed type-2 FBLS was able to maintain the fast computational nature of BLS. Third, a theoretical analysis on the convergence of type-2 FBLS was given to show the computational efficiency. Finally, some benchmark and practical problems were used to test the merits of type-2 FBLS. The experimental results indicated that the proposed type-2 FBLS can achieve outstanding performance.

ACS Style

Honggui Han; Zheng Liu; Hongxu Liu; Junfei Qiao; C. L. Philip Chen. Type-2 Fuzzy Broad Learning System. IEEE Transactions on Cybernetics 2021, PP, 1 -12.

AMA Style

Honggui Han, Zheng Liu, Hongxu Liu, Junfei Qiao, C. L. Philip Chen. Type-2 Fuzzy Broad Learning System. IEEE Transactions on Cybernetics. 2021; PP (99):1-12.

Chicago/Turabian Style

Honggui Han; Zheng Liu; Hongxu Liu; Junfei Qiao; C. L. Philip Chen. 2021. "Type-2 Fuzzy Broad Learning System." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 22 April 2021 in IEEE Transactions on Neural Networks and Learning Systems
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Face hallucination technologies have been widely developed during the past decades, among which the sparse manifold learning (SML)-based approaches have become the popular ones and achieved promising performance. However, these SML methods always failed in handling noisy images due to the least-square regression (LSR) they used for error approximation. To this end, we propose, in this article, a smooth correntropy representation (SCR) model for noisy face hallucination. In SCR, the correntropy regularization and smooth constraint are combined into one unified framework to improve the resolution of noisy face images. Specifically, we introduce the correntropy induced metric (CIM) rather than the LSR to regularize the encoding errors, which admits the proposed method robust to noise with uncertain distributions. Besides, the fused LASSO penalty is added into the feature space to ensure similar training samples holding similar representation coefficients. This encourages the SCR not only robust to noise but also can well exploit the inherent typological structure of patch manifold, resulting in more accurate representations in noise environment. Comparison experiments against several state-of-the-art methods demonstrate the superiority of SCR in super-resolving noisy low-resolution (LR) face images.

ACS Style

Licheng Liu; Qiying Feng; C. L. Philip Chen; YaoNan Wang. Noise Robust Face Hallucination Based on Smooth Correntropy Representation. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -13.

AMA Style

Licheng Liu, Qiying Feng, C. L. Philip Chen, YaoNan Wang. Noise Robust Face Hallucination Based on Smooth Correntropy Representation. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-13.

Chicago/Turabian Style

Licheng Liu; Qiying Feng; C. L. Philip Chen; YaoNan Wang. 2021. "Noise Robust Face Hallucination Based on Smooth Correntropy Representation." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-13.

Journal article
Published: 21 April 2021 in IEEE Transactions on Automatic Control
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This note presents a global adaptive asymptotic tracking control method, capable of guaranteeing prescribed transient behavior for uncertain strict-feedback nonlinear systems with arbitrary relative degree and unknown control directions. Unlike most existing funnel controls that are built upon time-varying feedback gains, the proposed method is derived from a tracking error-dependent normalized function and a barrier function, together with a time-varying scaling transformation, leading to an improved prescribed performance control solution with the following features: 1) the developed control is embedded with adaptive tuning and is able to ensure asymptotic tracking; 2) given transient performance is guaranteed in that the tracking error preserves in the prescribed boundary for $\forall t\ge 0$ ; and 3) it is able to cope with nonlinear systems with arbitrary relative degree, mismatched uncertainties and unknown control directions. Both theoretical analysis and numerical simulations verify the effectiveness and benefits of the proposed method.

ACS Style

Kai Zhao; Yongduan Song; C. L. Philip Chen; Long Chen. Adaptive Asymptotic Tracking with Global Performance for Nonlinear Systems with Unknown Control Directions. IEEE Transactions on Automatic Control 2021, PP, 1 -1.

AMA Style

Kai Zhao, Yongduan Song, C. L. Philip Chen, Long Chen. Adaptive Asymptotic Tracking with Global Performance for Nonlinear Systems with Unknown Control Directions. IEEE Transactions on Automatic Control. 2021; PP (99):1-1.

Chicago/Turabian Style

Kai Zhao; Yongduan Song; C. L. Philip Chen; Long Chen. 2021. "Adaptive Asymptotic Tracking with Global Performance for Nonlinear Systems with Unknown Control Directions." IEEE Transactions on Automatic Control PP, no. 99: 1-1.

Journal article
Published: 14 April 2021 in IEEE Transactions on Neural Networks and Learning Systems
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There are two main categories of face sketch synthesis: data- and model-driven. The data-driven method synthesizes sketches from training photograph-sketch patches at the cost of detail loss. The model-driven method can preserve more details, but the mapping from photographs to sketches is a time-consuming training process, especially when the deep structures require to be refined. We propose a face sketch synthesis method via regularized broad learning system (RBLS). The broad learning-based system directly transforms photographs into sketches with rich details preserved. Also, the incremental learning scheme of broad learning system (BLS) ensures that our method easily increases feature mappings and remodels the network without retraining when the extracted feature mapping nodes are not sufficient. Besides, a Bayesian estimation-based regularization is introduced with the BLS to aid further feature selection and improve the generalization ability and robustness. Various experiments on the CUHK student data set and Aleix Robert (AR) data set demonstrated the effectiveness and efficiency of our RBLS method. Unlike existing methods, our method synthesizes high-quality face sketches much efficiently and greatly reduces computational complexity both in the training and test processes.

ACS Style

Ping Li; Bin Sheng; C. L. Philip Chen. Face Sketch Synthesis Using Regularized Broad Learning System. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -15.

AMA Style

Ping Li, Bin Sheng, C. L. Philip Chen. Face Sketch Synthesis Using Regularized Broad Learning System. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Ping Li; Bin Sheng; C. L. Philip Chen. 2021. "Face Sketch Synthesis Using Regularized Broad Learning System." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-15.

Research article
Published: 31 March 2021 in Analytical Chemistry
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Abnormal gastric pH (pH > 3) has instructive significance for early diagnosis of various diseases, including cancer. However, for low patient compliance, limited penetration depth, high dependence on physiological function or unsafety issue, in situ noninvasive monitoring gastric pH is challenged. Herein, we developed a hydrogel capsule isolated human serum albumin-manganese complex (HSA-Mn) for in situ magnetic resonance imaging (MRI) gastric pH monitoring for the first time. In this strategy, the rotation motion restriction of Mn2+ after binding to HSA significantly increased the R1 (longitudinal relaxation rate) signal, and its high correlation with protonation imparted the HSA-Mn system sensitive responsiveness to varying pH (R1(pH 7)/R1(pH 1) = 8.2). Moreover, a screw jointed hydrogel capsule with signal confinement and internal standard abilities was designed. Such a nanoporous hydrogel capsule with size selectivity to surrounding molecules enabled a stable and sensitive response to different pH simulated gastric fluid within 0.5 h. In addition, with the unique structural outline and stable MRI characteristics, the capsule could also work as an internal standard, which facilitates the collection of signals and trace of the capsule in vivo. Through validating in a rabbit model, the precise abnormal gastric pH recognition capacity of the HSA-Mn hydrogel capsule was amply confirmed. Hence, the hydrogel capsule isolated HSA-Mn system strategy with great biocompatibility could be expected to be a potent tool for in situ anti-disturbance MRI of gastric pH in future clinical application.

ACS Style

Yiting Xu; Yanxia Yang; Zhiwei Yin; Xinqi Cai; Xin Xia; Michael J. Donovan; Long Chen; Zhuo Chen; Weihong Tan. In Situ Gastric pH Imaging with Hydrogel Capsule Isolated Paramagnetic Metallo-albumin Complexes. Analytical Chemistry 2021, 93, 5939 -5946.

AMA Style

Yiting Xu, Yanxia Yang, Zhiwei Yin, Xinqi Cai, Xin Xia, Michael J. Donovan, Long Chen, Zhuo Chen, Weihong Tan. In Situ Gastric pH Imaging with Hydrogel Capsule Isolated Paramagnetic Metallo-albumin Complexes. Analytical Chemistry. 2021; 93 (14):5939-5946.

Chicago/Turabian Style

Yiting Xu; Yanxia Yang; Zhiwei Yin; Xinqi Cai; Xin Xia; Michael J. Donovan; Long Chen; Zhuo Chen; Weihong Tan. 2021. "In Situ Gastric pH Imaging with Hydrogel Capsule Isolated Paramagnetic Metallo-albumin Complexes." Analytical Chemistry 93, no. 14: 5939-5946.

Journal article
Published: 31 March 2021 in IEEE Transactions on Neural Networks and Learning Systems
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Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of the search cost. Moreover, time-consuming model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from a prohibitive performance drop in the phase of architecture estimation. In this article, we propose a broad neural architecture search (BNAS) where we elaborately design broad scalable architecture dubbed broad convolutional neural network (BCNN) to solve the above issue. On the one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt RL and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling (GAP) layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS that modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in RL-based NAS approaches; 2) compared with small-size (0.5 million parameters) and medium-size (1.1 million parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-10; and 3) the learned architecture achieves 25.3% top-1 error on ImageNet just using 3.9 million parameters.

ACS Style

Zixiang Ding; Yaran Chen; Nannan Li; Dongbin Zhao; Zhiquan Sun; C. L. Philip Chen. BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -15.

AMA Style

Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, Zhiquan Sun, C. L. Philip Chen. BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Zixiang Ding; Yaran Chen; Nannan Li; Dongbin Zhao; Zhiquan Sun; C. L. Philip Chen. 2021. "BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-15.

Journal article
Published: 25 March 2021 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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The search for new approaches for output feedback control of uncertain nonlinear systems with unknown input and output hysteresis is an interesting problem in control theory. One challenging issue obstructs the development of output feedback control design is that both the genuine system input and output are unknown signals and unable to be employed in the observer and controller design. To obviate such obstruction, a new control design framework for adaptively compensating the input and output hysteresis is proposed with two adaptive hysteresis inverse operators, which are also utilized to develop a novel adaptive hysteresis operator-based filter. It is proved that with the proposed control scheme, all the closed-loop signals are bounded and the tracking error ultimately converges to a tunable residual around zero. Simulation studies demonstrate the methods developed.

ACS Style

Kaixin Lu; Zhi Liu; C. L. Philip Chen; Yun Zhang. Adaptive Inverse Compensation for Unknown Input and Output Hysteresis Using Output Feedback Neural Control. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -13.

AMA Style

Kaixin Lu, Zhi Liu, C. L. Philip Chen, Yun Zhang. Adaptive Inverse Compensation for Unknown Input and Output Hysteresis Using Output Feedback Neural Control. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-13.

Chicago/Turabian Style

Kaixin Lu; Zhi Liu; C. L. Philip Chen; Yun Zhang. 2021. "Adaptive Inverse Compensation for Unknown Input and Output Hysteresis Using Output Feedback Neural Control." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-13.

Journal article
Published: 17 March 2021 in IEEE Transactions on Cybernetics
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In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overview of the BLS in data mining and neural networks for the first time, focusing on summarizing various BLS methods from the aspects of its algorithms, theories, applications, and future open research questions. First, we introduce the basic pattern of BLS manifestation, the universal approximation capability, and essence from the theoretical perspective. Furthermore, we focus on BLS's various improvements based on the current state of the theoretical research, which further improves its flexibility, stability, and accuracy under general or specific conditions, including classification, regression, semisupervised, and unsupervised tasks. Due to its remarkable efficiency, impressive generalization performance, and easy extendibility, BLS has been applied in different domains. Next, we illustrate BLS's practical advances, such as computer vision, biomedical engineering, control, and natural language processing. Finally, the future open research problems and promising directions for BLSs are pointed out.

ACS Style

Xinrong Gong; Tong Zhang; C. L. Philip Chen; Zhulin Liu. Research Review for Broad Learning System: Algorithms, Theory, and Applications. IEEE Transactions on Cybernetics 2021, PP, 1 -29.

AMA Style

Xinrong Gong, Tong Zhang, C. L. Philip Chen, Zhulin Liu. Research Review for Broad Learning System: Algorithms, Theory, and Applications. IEEE Transactions on Cybernetics. 2021; PP (99):1-29.

Chicago/Turabian Style

Xinrong Gong; Tong Zhang; C. L. Philip Chen; Zhulin Liu. 2021. "Research Review for Broad Learning System: Algorithms, Theory, and Applications." IEEE Transactions on Cybernetics PP, no. 99: 1-29.

Journal article
Published: 10 March 2021 in Neurocomputing
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In this study, an adaptive neural tracking control problem for uncertain switched nonlinear systems with state quantization under arbitrary switching is investigated. A command-filtered backstepping control design strategy is implemented to overcome difficulties that the time derivate of common virtual control signals cannot be well defined. By deriving closed-loop system quantized errors bounded, quantized states can be used to control design and unquantized states can be applied to the stability analysis. And then, an adaptive neural tracking controller for switched nonlinear systems with state quantization via common Lyapunov function is proposed, which guarantees that all signals of closed-loop system remain semiglobal uniform ultimate boundedness and the genuine output of system can well track the reference trajectory. Finally, the proposed method is demonstrated by two simulation results.

ACS Style

Danping Zeng; Zhi Liu; C.L. Philip Chen; Yun Zhang. Adaptive neural tracking control for switched nonlinear systems with state quantization. Neurocomputing 2021, 454, 392 -404.

AMA Style

Danping Zeng, Zhi Liu, C.L. Philip Chen, Yun Zhang. Adaptive neural tracking control for switched nonlinear systems with state quantization. Neurocomputing. 2021; 454 ():392-404.

Chicago/Turabian Style

Danping Zeng; Zhi Liu; C.L. Philip Chen; Yun Zhang. 2021. "Adaptive neural tracking control for switched nonlinear systems with state quantization." Neurocomputing 454, no. : 392-404.

Journal article
Published: 04 March 2021 in IEEE Transactions on Cybernetics
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Sentiment analysis uses a series of automated cognitive methods to determine the author's or speaker's attitudes toward an expressed object or text's overall emotional tendencies. In recent years, the growing scale of opinionated text from social networks has brought significant challenges to humans' sentimental tendency mining. The pretrained language model designed to learn contextual representation achieves better performance than traditional learning word vectors. However, the existing two basic approaches for applying pretrained language models to downstream tasks, feature-based and fine-tuning methods, are usually considered separately. What is more, different sentiment analysis tasks cannot be handled by the single task-specific contextual representation. In light of these pros and cons, we strive to propose a broad multitask transformer network (BMT-Net) to address these problems. BMT-Net takes advantage of both feature-based and fine-tuning methods. It was designed to explore the high-level information of robust and contextual representation. Primarily, our proposed structure can make the learned representations universal across tasks via multitask transformers. In addition, BMT-Net can roundly learn the robust contextual representation utilized by the broad learning system due to its powerful capacity to search for suitable features in deep and broad ways. The experiments were conducted on two popular datasets of binary Stanford Sentiment Treebank (SST-2) and SemEval Sentiment Analysis in Twitter (Twitter). Compared with other state-of-the-art methods, the improved representation with both deep and broad ways is shown to achieve a better F1-score of 0.778 in Twitter and accuracy of 94.0% in the SST-2 dataset, respectively. These experimental results demonstrate the abilities of recognition in sentiment analysis and highlight the significance of previously overlooked design decisions about searching contextual features in deep and broad spaces.

ACS Style

Tong Zhang; Xinrong Gong; C. L. Philip Chen. BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis. IEEE Transactions on Cybernetics 2021, PP, 1 -12.

AMA Style

Tong Zhang, Xinrong Gong, C. L. Philip Chen. BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis. IEEE Transactions on Cybernetics. 2021; PP (99):1-12.

Chicago/Turabian Style

Tong Zhang; Xinrong Gong; C. L. Philip Chen. 2021. "BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 02 March 2021 in IEEE Transactions on Neural Networks and Learning Systems
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In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.

ACS Style

Guangzhu Peng; C. L. Philip Chen; Chenguang Yang. Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -11.

AMA Style

Guangzhu Peng, C. L. Philip Chen, Chenguang Yang. Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-11.

Chicago/Turabian Style

Guangzhu Peng; C. L. Philip Chen; Chenguang Yang. 2021. "Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-11.